Fast human action classification and VOI localization with enhanced sparse coding
Journal of Visual Communication and Image Representation
Large scale continuous visual event recognition using max-margin Hough transformation framework
Computer Vision and Image Understanding
Editor's Choice Article: Human activity recognition in videos using a single example
Image and Vision Computing
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Despite recent successes of searching small object in images, it remains a challenging problem to search and locate actions in crowded videos because of (1) the large variations of human actions and (2) the intensive computational cost of searching the video space. To address these challenges, we propose a fast action search and localization method that supports relevance feedback from the user. By characterizing videos as spatio-temporal interest points and building a random forest to index and match these points, our query matching is robust and efficient. To enable efficient action localization, we propose a coarse-to-fine sub-volume search scheme, which is several orders faster than the existing video branch and bound search. The challenging cross-dataset search of several actions validates the effectiveness and efficiency of our method.